- Author - Pramod Dutta
- Website -https://sdet.live/become
| Module | Day | Topic | Learning Objectives & Activities | Tools/Technologies Introduced |
|---|---|---|---|---|
| Module 1: AI Fundamentals & Prompt Engineering | 1 | Introduction to AI Terminologies & Course Overview | - Understand fundamental AI terminology relevant to testing - Overview of course structure and objectives - Explore the impact of AI on software testing - Introduction to the 30-day learning journey |
Course materials, AI glossary |
| 2 | Understanding AI Applications, LLMs & AI Agents | - Explore various AI applications in software testing - Understand Large Language Models (LLMs) and their capabilities - Learn about AI agents and their role in testing - Discuss real-world use cases |
LLMs (GPT, Claude, etc.), AI agent frameworks | |
| 3 | Privacy & Security of AI Applications | - Understand privacy concerns with AI tools - Learn security best practices when using AI - Explore data protection and compliance issues - Evaluate risks associated with AI in testing |
Security frameworks, Privacy tools | |
| 4 | Introduction to Prompt Engineering Fundamentals | - Learn what prompt engineering is and why it matters - Understand basic principles of effective prompting - Explore different types of prompts for testing - Practice simple prompt construction |
Prompt engineering techniques, Basic AI interfaces | |
| 5 | Setting up AI Tools (ChatGPT, Gemini, etc.) | - Set up accounts and access to various AI tools - Learn the interfaces and features of each tool - Compare capabilities of different AI platforms - Configure tools for optimal testing support |
ChatGPT, Gemini, Claude, other AI platforms | |
| 6 | Basic Prompt Engineering Techniques | - Master zero-shot and few-shot prompting - Learn context setting and role prompting - Practice prompt templates for testing scenarios - Understand how to refine prompts for better results |
Advanced prompting techniques, Prompt templates | |
| 7 | Practice Session: Writing Effective Prompts | - Apply learned techniques to real testing scenarios - Create prompts for test case generation - Develop prompts for bug analysis and reporting - Build a personal prompt library for testing tasks |
AI tools, Prompt documentation | |
| Module 2: Test Artifacts Generation with AI | 8 | Generating Test Plans using AI | - Learn techniques for creating comprehensive test plans - Use AI to analyze requirements and suggest test scope - Generate test plans with appropriate resource allocation - Evaluate AI-generated test plans for completeness |
Test planning tools, AI-assisted documentation |
| 9 | AI-Powered Test Case Generation | - Create effective prompts for test case generation - Generate functional and non-functional test cases - Learn to specify test data and expected outcomes - Evaluate and refine AI-generated test cases |
Test case management tools, AI generation techniques | |
| 10 | Creating Test Strategy with AI (Shift Left Testing) | - Use AI to develop comprehensive test strategies - Implement shift-left testing approaches with AI - Generate risk-based testing strategies - Create test approach recommendations for different project types |
Test strategy frameworks, Shift-left methodologies | |
| 11 | Generating Test Data Combinations using AI | - Learn techniques for generating diverse test data - Create prompts for boundary value and equivalence partition data - Generate data for complex scenarios and edge cases - Validate AI-generated test data for completeness |
Test data generation tools, Data validation techniques | |
| 12 | Creating Bug Templates and Reports with AI | - Generate comprehensive bug reports with AI assistance - Create bug templates for different types of defects - Use AI to analyze failure patterns and suggest root causes - Develop automated bug triage and prioritization |
Bug tracking systems, Report generation tools | |
| 13 | Test Requirements Analysis using AI | - Apply AI to analyze and clarify requirements - Identify ambiguous or conflicting requirements - Generate testable requirements from user stories - Create traceability matrices with AI assistance |
Requirements management tools, Analysis techniques | |
| 14 | Practice: Complete Test Artifact Creation Project | - Apply all learned techniques to a real project scenario - Generate a complete set of test artifacts using AI - Evaluate the quality and completeness of AI-generated artifacts - Refine prompts based on project outcomes |
Project scenarios, AI tools, Documentation platforms | |
| Module 3: AI for Test Automation | 15 | Generating Selenium Automation Code with AI | - Learn techniques for generating Selenium test code - Create prompts for specific Selenium functionality - Generate page object models and test methods - Refine and debug AI-generated Selenium code |
Selenium WebDriver, Page Object Model |
| 16 | Creating Playwright Test Scripts using AI | - Generate Playwright test scripts with AI assistance - Create scripts for cross-browser testing scenarios - Develop selectors and assertions with AI - Optimize Playwright scripts for maintainability |
Playwright, Cross-browser testing techniques | |
| 17 | Cypress Test Generation with AI | - Generate Cypress test scripts for web applications - Create custom commands and utilities with AI - Develop tests for specific Cypress features - Implement best practices in AI-generated Cypress code |
Cypress framework, Custom commands | |
| 18 | Cucumber Gherkin & Step Definitions with AI | - Generate Gherkin scenarios for BDD testing - Create step definitions from feature files - Develop domain-specific language with AI assistance - Implement parameterization and data tables in BDD |
Cucumber, Gherkin syntax, BDD frameworks | |
| 19 | Creating Custom Utility Code Methods | - Generate reusable utility methods with AI - Create helper functions for common testing tasks - Develop custom libraries for test automation - Implement error handling and logging utilities |
Utility libraries, Code optimization techniques | |
| 20 | Framework Configuration Files using AI | - Generate configuration files for test frameworks - Create environment-specific configurations - Develop data-driven testing configurations - Implement CI/CD pipeline configurations |
Configuration management, CI/CD tools | |
| 21 | Code Optimization & Standards with AI | - Use AI to review and optimize test code - Implement coding standards and best practices - Refactor AI-generated code for maintainability - Develop code documentation with AI assistance |
Code review tools, Documentation generators | |
| Module 4: Advanced AI Tools & Integration | 22 | Introduction to GitHub Copilot for Testing | - Set up and configure GitHub Copilot for testing - Learn Copilot's capabilities for test automation - Generate test code with Copilot suggestions - Evaluate Copilot's effectiveness for testing tasks |
GitHub Copilot, IDE integration |
| 23 | Advanced GitHub Copilot Tips & Tricks | - Master advanced Copilot features for testing - Create custom Copilot prompts for testing scenarios - Develop efficient workflows with Copilot - Integrate Copilot with existing testing frameworks |
Advanced Copilot features, Workflow optimization | |
| 24 | API Automation with RestAssured using AI | - Generate RestAssured test code with AI assistance - Create API test scripts for various HTTP methods - Develop API test data management with AI - Implement API test automation best practices |
RestAssured, API testing techniques | |
| 25 | JSON Parsing & POJO Class Generation | - Generate POJO classes from JSON schemas with AI - Create JSON parsing utilities with AI assistance - Develop data transformation and validation code - Implement complex JSON handling in test automation |
JSON processing libraries, POJO generation tools | |
| 26 | SQL Query Generation for Complex Databases | - Generate SQL queries for database testing with AI - Create complex queries for data validation - Develop database test automation scripts - Implement data-driven testing with SQL |
SQL, Database testing tools | |
| 27 | Introduction to AI-Powered Testing Tools | - Explore commercial AI testing platforms - Evaluate features and capabilities of AI testing tools - Compare different AI testing solutions - Select appropriate tools for specific testing needs |
AI testing platforms, Evaluation frameworks | |
| 28 | TestRigor: AI Tool for Codeless Automation | - Learn TestRigor's AI capabilities for testing - Create automated tests without coding - Implement self-healing tests with AI - Evaluate TestRigor's effectiveness for different testing scenarios |
TestRigor, Codeless automation | |
| 29 | AI Agents for Browser Automation Demo | - Understand AI agents for browser automation - Explore agent-based testing approaches - Implement autonomous testing with AI agents - Evaluate the benefits and limitations of AI agents |
AI agent frameworks, Browser automation tools | |
| 30 | Integration & Future of AI in Testing + Course Review | - Develop strategies for integrating AI into testing workflows - Explore emerging trends in AI for testing - Create a personal AI testing skills development plan - Review course learning and plan next steps |
Integration strategies, Future trends analysis |
- AI terminology and concepts relevant to software testing
- Understanding of LLMs and their capabilities in testing
- Privacy and security considerations when using AI tools
- Prompt engineering fundamentals and techniques
- Effective use of various AI platforms (ChatGPT, Gemini, etc.)
- Development of a personal prompt library for testing tasks
- Techniques for generating comprehensive test plans
- AI-powered test case generation for different testing types
- Creating effective test strategies with shift-left approaches
- Generating diverse and comprehensive test data combinations
- Creating detailed bug reports and templates
- Using AI for requirements analysis and traceability
- Practical application of AI for complete test artifact creation
- Generating automation code for popular frameworks (Selenium, Playwright, Cypress)
- Creating BDD test scenarios and step definitions with AI
- Developing custom utility code and helper functions
- Generating framework configuration files
- Code optimization and standards implementation with AI
- Best practices for using AI in test automation development
- Effective use of GitHub Copilot for testing tasks
- Advanced techniques for maximizing AI tool capabilities
- API automation with AI assistance using RestAssured
- JSON parsing and POJO class generation
- SQL query generation for database testing
- Evaluation and implementation of AI-powered testing tools
- Codeless automation with AI tools like TestRigor
- Understanding and implementing AI agents for browser automation
- Integration strategies and future trends in AI testing